Which tools and browser extensions can detect AI-generated videos on YouTube?
Executive summary
Several browser extensions and standalone services claim to detect AI‑generated or deepfake videos on YouTube, including AI Content Shield (Chrome/Firefox), Hive AI Detector (Chrome), Hiya’s Deepfake Voice Detector (Chrome), and a range of commercial detectors such as Sensity, Reality Defender, Sightengine, Deepware and others reviewed in industry roundups [1] [2] [3] [4] [5] [6] [7]. Independent research and academic toolkits also exist (GitHub listings, arXiv papers), but reporting and vendors warn detectors can fail in real‑world conditions and need human review [8] [9] [10].
1. What tools are being offered now — a quick inventory
Browser extensions that specifically advertise detection or blocking of AI videos on YouTube include AI Content Shield (Chrome and Firefox listings) which promises to block or tag AI content across YouTube and social platforms [1] [11], Hive AI Detector (Chrome) which can scan images, text, audio and video via right‑click or upload [2], Hiya’s Deepfake Voice Detector (Chrome extension coverage by PCMag) focused on audio deepfakes used in videos [3], and community extensions such as “Is Generated” that let users flag and block content [12]. Open‑source projects and prototypes also add YouTube hooks—examples include a GitHub DeepFakeChrome extension that integrates a detection button into the YouTube player [13].
2. Commercial services and specialist detectors
Multiple commercial platforms offer video deepfake/AI detection that organizations and individuals can use off‑site: Sensity, Reality Defender, Deepware, Sightengine and similar vendors are named in reviews and product pages as capable of scanning uploads or URLs to flag synthetic media [6] [5] [4] [7]. Aggregator/review sites list detection accuracy figures and rank options—Reality Defender and Sensity appear commonly cited as top choices in 2025 roundups [7] [6].
3. How these tools claim to work — and their limits
Vendors say detectors use multimodal analysis: visual artifacts, facial anomalies, lip‑sync mismatches, optical flow and temporal inconsistencies, audio signatures, metadata and sometimes biometrics or watermark checks [14] [4] [8]. Independent research groups are developing “universal” detectors that look beyond faces to backgrounds and temporal defects [9]. But industry reporting and reviews caution detectors can perform well in lab tests yet struggle in messy, real‑world YouTube content; experts recommend combining multiple detectors and human review [10] [7].
4. What YouTube/Google are doing and why it matters
YouTube is rolling out “likeness detection” to flag videos that use creators’ faces without permission; that system uses biometric matching and will be available first to a subset of creators, but experts told reporting that the feature raises privacy concerns because Google may use the data to train models [15]. YouTube’s own tools operate at “YouTube scale” to surface likely deepfakes to creators for review [15]. Google/YouTube also participate in research and watermarking discussions that could aid detection [9].
5. Accuracy, trust and the recommendation for users
Published vendor claims of high accuracy (many vendors or review sites quote figures like 90–98%) should be treated skeptically: review sites and academics repeatedly note detectors are an arms race—generators improve quickly and detectors can be brittle outside their test datasets [7] [10] [9]. PCMag’s coverage of Hiya makes the same point for audio detectors: useful, but not foolproof [3]. Best practice from reporting: use several tools (browser extension for quick scans + a specialist service for deeper analysis), examine metadata/transcripts and involve human forensic review when stakes are high [7] [10].
6. Privacy and governance tensions to watch
Extensions that scan pages or upload media raise privacy questions; AI Content Shield markets itself as “privacy‑first,” but YouTube’s likeness detection explicitly uses creators’ biometric templates and drew privacy criticism because of potential model training [16] [15]. Community extensions that crowdsource judgments (Is Generated) trade automated detection for human moderation biases [12]. Reporting shows detection tech and platform measures are also policy tools that can be used selectively—platforms decide who gets early access and how removals are handled [17] [15].
7. Bottom line for a YouTube viewer or creator
If you suspect a YouTube video is AI‑generated, quick options are browser extensions (AI Content Shield, Hive AI Detector, Hiya for audio) for surface signals and blocking [1] [2] [3]. For important verification, run the video through a specialist detector (Sensity, Reality Defender, Sightengine or Deepware as examples) and seek human forensic review; expect false positives/negatives and be aware detection remains imperfect in the wild [6] [4] [5] [7]. Available sources do not mention a single foolproof extension that reliably and consistently detects every AI‑generated YouTube video.
Limitations: this survey uses product pages, reviews and reporting included in the search set and does not test tools directly; vendors’ accuracy claims and lab figures require independent validation [7] [6].